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Selection of EAP-authentication methods in WLANs
IEEE 802.1X is a key part of IEEE802.11i. By employing Extensible Authentication Protocol (EAP) it supports a variety of upper layer
authentication methods each with different benefits and drawbacks. Any one of these authentication methods can be the ideal choice for a specific networking environment. The fact that IEEE 802.11i leaves the selection of the most suitable authentication method to system implementers makes the authentication framework more flexible, but on the other hand leads to the
question of how to select the authentication method that suits an organisation’s requirements and specific networking environment. This paper gives an overview of EAP authentication methods and provides a table comparing their properties. It then identifies the crucial factors to be considered when employing EAP authentication methods in WLAN environments. The paper presents algorithms that guide the selection of an EAP-authentication method for a WLAN and demonstrates their application through three examples
Machine Learning Aided Static Malware Analysis: A Survey and Tutorial
Malware analysis and detection techniques have been evolving during the last
decade as a reflection to development of different malware techniques to evade
network-based and host-based security protections. The fast growth in variety
and number of malware species made it very difficult for forensics
investigators to provide an on time response. Therefore, Machine Learning (ML)
aided malware analysis became a necessity to automate different aspects of
static and dynamic malware investigation. We believe that machine learning
aided static analysis can be used as a methodological approach in technical
Cyber Threats Intelligence (CTI) rather than resource-consuming dynamic malware
analysis that has been thoroughly studied before. In this paper, we address
this research gap by conducting an in-depth survey of different machine
learning methods for classification of static characteristics of 32-bit
malicious Portable Executable (PE32) Windows files and develop taxonomy for
better understanding of these techniques. Afterwards, we offer a tutorial on
how different machine learning techniques can be utilized in extraction and
analysis of a variety of static characteristic of PE binaries and evaluate
accuracy and practical generalization of these techniques. Finally, the results
of experimental study of all the method using common data was given to
demonstrate the accuracy and complexity. This paper may serve as a stepping
stone for future researchers in cross-disciplinary field of machine learning
aided malware forensics.Comment: 37 Page
Biometric identity-based cryptography for e-Government environment
Government information is a vital asset that must be kept in a trusted environment and efficiently managed by authorised parties. Even though e-Government provides a number of advantages, it also introduces a range of new security risks. Sharing confidential and top-secret information in a secure manner among government sectors tend to be the main element that government agencies look for. Thus, developing an effective methodology is essential and it is a key factor for e-Government success. The proposed e-Government scheme in this paper is a combination of identity-based encryption and biometric technology. This new scheme can effectively improve the security in authentication systems, which provides a reliable identity with a high degree of assurance. In addition, this paper demonstrates the feasibility of using Finite-state machines as a formal method to analyse the proposed protocols
Intangible trust requirements - how to fill the requirements trust "gap"?
Previous research efforts have been expended in terms of the capture and subsequent instantiation of "soft" trust requirements that relate to HCI usability concerns or in relation to "hard" tangible security requirements that primarily relate to security a ssurance and security protocols. Little direct focus has been paid to managing intangible trust related requirements
per se. This 'gap' is perhaps most evident in the public B2C (Business to Consumer) E- Systems we all use on a daily basis. Some speculative suggestions are made as to how to fill the 'gap'.
Visual card sorting is suggested as a suitable evaluative tool; whilst deontic logic trust norms
and UML extended notation are the suggested (methodologically invariant) means by which software development teams can perhaps more fully capture hence visualize intangible trust requirements
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